Chad Gordon
Quick update on our original post. We've mostly solved the "players switching sides while jumping" problem and have also jumped into the rabbit hole of ball tracking as well. I'm not going to get into the technical details here - more just give a glimpse into what we've been working on and where we are [...]
% split of Terminal vs. Non-Terminal attacks 1. Welcome to attacking: the most important skill in our game. I'd also argue it's the most interesting, least understood, and most dynamic of our game - making it the best place to pick some low-hanging fruit. 2. We need to understand the reality of attacking. As you [...]
Figured we'd throw a super quick Final Four + Stivrins post together just to wrap up the 2021 season as everyone shifts their attention to the transfer portal and freaks out about the seasonal coaching reshuffle. Starting with the semifinals, below are the Match Win Probability charts - visually describing a team's likelihood to win [...]
So...what was the point again? Honestly, we didn't set out to make a ratings system. We were building our xPWP model (expected Point Win Probability) and needed a reasonably accurate estimate of the initial chance of each team winning the point (ex: Nebraska serving to Wisconsin, before the serve happens, how likely is Nebraska to [...]
Computer vision already powers self-driving cars, the gaming world, and numerous other products. Within sport, this technology has already made its way into basketball, football, baseball, soccer, etc. - and has further fueled their respective analytics movements. One of my favorite articles that encapsulates what this work allows for is from Brian Macdonald. Published in [...]
December 18th: Updated predictions moving into the Finals are live Had a great week in Columbus - and now we're down to the final two. Our model likes Wisconsin over Nebraska 58/42 But personally...I think Nebraska is on a heater and they're gonna take this thing tonight...we shall see. December 14th: Updated predictions moving into [...]
Motivation While trying to adjust our expected points (PWP) model to account for team and opponent strength, we ran into a few issues. As far as we could tell, the only analytics-based rating system out there was the Pablo rankings, and we wanted ratings that (1) are not paywalled, (2) can be reproduced by other [...]
This marks our first public unveiling of our new rating system [link to the rating system explainer post]. Our goal will be to update these rankings every Monday evening / Tuesday morning. First and foremost, huge thanks to Professor Dwight Wynne for doing the heavy lifting and getting this system off the ground. I'm excited [...]
Dwight Wynne teaches statistics at California State University, Fullerton and supervises student projects helping their women's volleyball team get more out of their data. Introduction One tool that has become increasingly common in the modern consumption of sports analytics is a win probability graph. A win probability graph shows, given the current score and time [...]
Introduction Having a lead in a set conveys confidence that we are more likely to win. We should be even more confident if that lead is near the end of the set, unless we are matched against a much better opponent. Using a lot of data and some modeling, we can quantify that confidence into [...]
Madeline Gates - 2019 Stanford University Interactive Dashboard: https://public.tableau.com/views/SeasonTrends/HittingEfficiency?:language=en&:display_count=y&publish=yes&:origin=viz_share_link Is your team getting better or worse as the season progresses? Are your players developing and performing stronger as the year goes on? In the chart above, we take a look at Madeline Gates, the middle from Stanford. We're looking at a rolling average of her [...]
Rethinking Volley 